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The Korean Society for Journalism & Communication (KSJCS)
[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 67, No. 3, pp.80-126
ISSN: 2586-7369 (Online)
Print publication date 30 Jun 2023
Received 16 Dec 2022 Accepted 17 May 2023 Revised 31 May 2023
DOI: https://doi.org/10.20879/kjjcs.2023.67.3.003

코로나19와 분노유발보도 : 시기별 주요 토픽과 부정정서의 전이 현상을 중심으로

이훈*** ; 이현수 ; 연지영 ; 심홍진****
***경희대학교 미디어학과 부교수 hoonlz@khu.ac.kr
고려대학교 미디어학과 박사과정
경희대학교 미디어학과 석사
****정보통신정책연구원 지능정보사회정책센터 연구위원 hjshim@kisdi.re.kr
COVID-19 and Anger-Inducing News Coverage : Focusing on Major Topics and the Transition of Negative Emotions
Hoon Lee*** ; Hyunsoo Lee ; Jiyoung Yeon ; Hongjin Shim****
***Associate Professor, Department of Media, Kyung Hee University, first author hoonlz@khu.ac.kr
Ph.D. Student, Department of Media, Korea University
M.A. Graduate, Department of Media, Kyung Hee University
****Research Fellow, Center for AI and Social Policy, Korea Information Society Development Institute, corresponding author hjshim@kisdi.re.kr

초록

이 연구는 코로나19 기간 동안 뉴스 기사가 만들어내는 분노, 즉 분노유발보도를 분석하여 언론보도가 여론에 어떠한 영향을 미치는지를 알아보고자 하였다. 구체적으로 이 연구의 목적은 언론이 코로나19와 관련하여 어떠한 주제와 이슈에 주목했는지를 빅데이터 분석을 통해 국내 포털(네이버, 다음)의 기사(분노유발보도)를 살펴보는 것이다. 또 이 연구는 빅데이터 분석을 통해 검토한 분노유발보도가 실제 여론을 어떠한 방향으로 이끌고 형성하는지를 실증하였다. 인지평가이론, 침묵의 나선 이론, 정보각성가설 등을 이론적 프레임으로 활용해 코로나 19 관련 보도의 시기별 토픽, 분노유발보도의 감정적 전이, 포털 간 분노유발보도 토픽의 상이성을 정교하게 분석했다. 이를 위해 이 연구는 자동화된 내용분석(computerized content analysis) 방법과 딥러닝에 기반하여 기사의 토픽 모델링과 기사 및 댓글의 감성분석을 실시했다. 이 과정에서 2020년 1월 1일부터 2021년 8월 31일까지 약 18개월 동안 ‘네이버’와 ‘다음’이 제공하는 언론사별 기사를 수집, 활용하였다. 분석결과, 추출된 토픽 개수는 ‘코비드 확산’, ‘경제 상황’, ‘사회 변화상’ 등 9개였으며, 포털 간 각 토픽의 분기별 기사 수의 변화도 관찰되었다. 또 감성분석 결과는 특정 주제에 대한 기사의 부정적 논조가 기사의 부정적 여론 형성, 즉 감정의 전이를 유발하고 있음을 보여줬다. 이 연구는 딥러닝이 가미된 빅데이터 분석기법을 통해 단순히 현상을 기술하는 것이 아닌 침묵의 나선 이론, 정보각성가설 등 이론에 기초한 미디어의 영향이나 효과를 정교하게 분석했다는 데서 방법론적 함의를 찾을 수 있다. 또 크롤링으로 대량의 기사와 댓글을 표집하고 이를 분석 데이터로 활용해 코로나19와 관련된 분노유발보도의 동향과 이에 따른 수용자의 반응을 전반적으로 파악했다는 점에서도 또 다른 의의를 찾을 수 있다.

Abstract

In order to research how media reports affect public opinion, the main goal of this study is to analyze the anger elicited by news articles during the COVID-19 epidemic, with a focus on anger-inducing coverage. In particular, the study aims to scrutinize the articles (which serve as anger-inducing reports) from Naver and Daum, prominent domestic portals in Korea, to identify the specific topics and issues that received substantial attention from the media about COVID-19. By delving into these articles, the study seeks to unravel the underlying factors contributing to the generation of anger and assesses how the coverage shapes and influences public sentiment and viewpoint. To this end, we leverage big data analytics and investigate what topics and issues have attracted media attention and how they have been addressed. This study also attended to how anger-inducing coverage reviewed through big data analysis shapes actual public opinion. Cognitive appraisal theory, spiral of silence theory, and information awareness hypothesis were used as theoretical grounds to analyze the differences between COVID-19-related coverage, emotional transfer of anger-inducing coverage, and anger-inducing coverage between portals. With these goals in mind, this study conducted topic modeling of articles and emotional analysis of articles as well as users comments based on automated content analysis methods and deep learning. In this process, articles from news media available through Naver and Daum were collected for about 18 months from January 1, 2020, to August 31, 2021. As a result of the analysis, nine distinct topics were identified, including 'Spread of COVID-19', 'Economic situation', and 'Vaccination', and the change in the number of articles by a quarter of each topic between portals was also observed. In addition, the findings obtained from the emotional analysis revealed a significant correlation between the negative tone employed in articles discussing specific topics and the formation of negative public opinion. In other words, it was observed that a pessimistic or unfavorable stance within an article directly impacted the transfer of emotions to the readers, ultimately shaping their perception and attitude. This study has methodological ramifications because it examines subtle media effects based on theories like the spiral of silence theory and information awareness hypothesis, rather than just describing the phenomenon through big data analysis techniques with deep learning. Another implication is that a large number of articles and comments were gathered through crawling and used as analysis data to discover the patterns of anger-inducing coverage of COVID-19 and audience responses.

Keywords:

Covid-19, Anger-Inducing Coverage, Topic Modeling, Portals, The Transfer of Emotions

키워드:

코비드19, 분노유발보도, 토픽 모델링, 포털, 감정적 전이

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of the Republic of Korea(NRF-2020S1A5A8040148)(본 논문은 2020년 대한민국 교육부와 한국연구재단의 인문사회학분야 신진연구자지원을 받아 수행된 연구임(NRF-2020S1A5A8040148)).

This study reveals that it uses data from the “‘Post-COVID-19 era’, the study on the problems of hate-causing coverage and the direction of policy”(2021, Korea Information Society Development Institute)(이 연구는 “‘포스트 코로나 시대’, 혐오유발보도의 문제점 및 정책적 대응 방향에 관한 연구”(2021, 정보통신정책연구원)보고서의 데이터를 활용한 것임을 밝힙니다).

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Appendix

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